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基于社会网络视角的交通仿真和计算实验研究文献分析

叶佩军 吕宜生 吉竟初

叶佩军, 吕宜生, 吉竟初. 基于社会网络视角的交通仿真和计算实验研究文献分析. 自动化学报, 2013, 39(9): 1402-1412. doi: 10.3724/SP.J.1004.2013.01402
引用本文: 叶佩军, 吕宜生, 吉竟初. 基于社会网络视角的交通仿真和计算实验研究文献分析. 自动化学报, 2013, 39(9): 1402-1412. doi: 10.3724/SP.J.1004.2013.01402
YE Pei-Jun, Lü Yi-Sheng, JI Jing-Chu. Literature Analysis for Traffic Simulation and Computational Experiments Based on Social Networks. ACTA AUTOMATICA SINICA, 2013, 39(9): 1402-1412. doi: 10.3724/SP.J.1004.2013.01402
Citation: YE Pei-Jun, Lü Yi-Sheng, JI Jing-Chu. Literature Analysis for Traffic Simulation and Computational Experiments Based on Social Networks. ACTA AUTOMATICA SINICA, 2013, 39(9): 1402-1412. doi: 10.3724/SP.J.1004.2013.01402

基于社会网络视角的交通仿真和计算实验研究文献分析


DOI: 10.3724/SP.J.1004.2013.01402
详细信息
    作者简介:

    叶佩军 中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生. 2008年获大连交通大学电气信息学院自动化专业学士学位. 主要研究方向为平行交通管控,交通流建模与优化,交通仿真,智能交通系统.E-mail: dreamflight@163.com

  • 基金项目:

    国家自然科学基金(60921061, 70890084, 60974095, 60904057, 612 03166)资助

Literature Analysis for Traffic Simulation and Computational Experiments Based on Social Networks

More Information
  • Fund Project:

    Supported by National Natural Science Foundation of China (60921061, 70890084, 60974095, 60904057, 61203166)

  • 摘要: 交通仿真和计算实验作为交通科学问题研究和工程应用实践中的重要方法和手段, 受到越来越多的关注.本文从社会网络角度, 分析了2000年~2012年来, ISI Web of Science (WoS) 收录的关于交通仿真及计算实验研究的文献. 本文工作分为三部分: 首先, 分析了近13年来该领域每年论文的发表趋势; 其次, 引入社会网络, 从论文数量、影响力、合作关系和知识传播度四个方面考察了关键学者, 并给出了其合作关系聚类图; 最后, 仍然从上述四个方面考察了本领域内关键的研究机构. 结果表明: 该领域的研究成果和研究机构增长迅速; 学者之间的合作关系非常广泛且极为复杂; 合作的广泛程度与知识传播度并无明显的相关性; 研究机构层面的合作呈现分散状态, 且合作单位数量较少.
  • [1] Downs A. The law of peak-hour expressway congestion. Traffic Quarterly, 1962, 16(3): 393-409
    [2] The TRANSYT website [Online], available: https://www. trlsoftware.co.uk/products/junction_signal_design/transyt, April 17, 2013
    [3] Gantz D T, Mekemson J R. Flow profile comparison of a microscopic car-following model and a macroscopic platoon dispersion model for traffic simulation. In: Proceedings of the 22nd Conference on Winter Simulation. New Orleans, LA: IEEE Press Piscataway, 1990. 770-774
    [4] Kuhne R D, Rodiger M B. Macroscopic simulation model for freeway traffic with jams and stop-start waves. In: Proceedings of the 23rd Conference on Winter Simulation. Phoenix, AZ: IEEE Computer Society, 1991. 762-770
    [5] Hoogendoorn S P, Bovyph H L. New estimation technique for vehicle-type-specific headway distribution. Transportation Research Record, 1998, 1646(1): 18-28
    [6] Yang Q. A Simulation Laboratory for Evaluating Dynamic Traffic Management Systems [Ph.D. dissertation], Center for Transportation Studies, Massachusetts Institute of Technology, USA, 1999
    [7] Burghout W. Hybrid Microscopic-Mesoscopic Traffic Simulation [Ph.D. dissertation], Royal Institute of Technology, Stockholm, 2004
    [8] Helbing D. Verkehrsdynamik: Neue Physikalische Modellierungskonzepte. Berlin, Heidelberg: Springer-Verlag, 1997
    [9] da Silva B C, Bazzan A L C, Andriotti G K, Lopes F, de Oliveira D. Itsumo: an intelligent transportation system for urban mobility. In: Proceedings of the 4th International Conference on Innovative Internet Community Systems. New York, NY, USA: Springer-Verlag, 2006. 224-235
    [10] Radecký M, Gajdoš P. Intelligent agents for traffic simulation. In: Proceedings of the 2008 Spring Simulation Multiconference. San Diego, CA, USA: Society for Computer Simulation International, 2008. 109-115
    [11] Schaefer L A, Mackulak G T, Cochran J, Cherilla J L. Application of a general particle system model to movement of pedestrians and vehicles. In: Proceedings of the 30th Conference on Winter Simulation. Los Alamitos, CA, USA: IEEE Computer Society Press, 1998. 1155-1160
    [12] Brackstone M, McDonald M. Car-following: a historical review. Transportation Research Part F: Traffic Psychology and Behaviour, 1999, 2(4): 181-196
    [13] Helbing D, Hennecke A, Shvetsov V, Treiber M. Micro-and macro-simulation of freeway traffic. Mathematical and Computer Modelling, 2002, 35(5-6): 517-547
    [14] Zou Z J, Yang D Y. A comprehensive review of road traffic simulation research. Journal of Traffic and Transportation Engineering, 2001, 1(2): 88-91
    [15] Guo X, Wang H. Traffic analysis and simulation software: research progress and prospect. Journal of Central South Highway Engineering, 2005, 30(1): 144-149
    [16] Zhang Q P, Feng Z, Li X, Zheng X L, Zhang L. 25 years of collaborations in IEEE intelligent systems. IEEE Intelligent Systems, 2010, 25(6): 67-75
    [17] Daganzo C F. A behavioral theory of multi-lane traffic flow, Part I: Long homogeneous freeway sections. Transportation Research Part B: Methodological, 2002, 36(2): 131-158
    [18] Daganzo C F. A behavioral theory of multi-lane traffic flow, Part II: Merges and the onset of congestion. Transportation Research Part B: Methodological, 2002, 36(2): 159-169
    [19] Daganzo C F. In traffic flow, cellular automata = kinematic waves. Transportation Research Part B: Methodological, 2006, 40(5): 396-403
    [20] Daganzo C F. On the variational theory of traffic flow: well-posedness, duality and applications. Networks and Heterogeneous Media, 2006, 1(4): 601-619
    [21] Mahmassani H S, Jayakrishnan R, Herman R. Microscopic simulation of traffic in networks: supercomputer experience. Journal of Computing in Civil Engineering, 1990, 4(1): 1-19
    [22] Sbayti H, Lu C C, Mahmassani H S. Efficient implementation of method of successive averages in simulation-based dynamic traffic assignment models for large-scale network applications. Transportation Research Record, 2007, 2029: 22-30
    [23] Mahmassani H S, Hu T, Peeta S. Microsimulation-based procedures for dynamic network traffic assignment. In: Proceedings of the 22nd Planning and Transport Research and Computation European Transport Forum. University of Warwick, UK: PTRC, 1994. 53-64
    [24] Peeta S, Pasupathy R. Analyzing traffic system evolution using multi-agent simulation. In: Proceedings of the 80th Annual Meeting of the Transportation Research Board. Washington, D. C., USA, 2001.
    [25] Peeta S, Ramos J L. Driver response to variable message signs-based traffic information. IEE Proceedings on Intelligent Transport Systems, 2006, 153(1): 2-10
    [26] Wang F Y, Tang S M. A framework for artificial transportation systems: from computer simulations to computational experiments. In: Proceedings of the 2005 IEEE International Conference on Intelligent Transportation Systems, Vienna, Austria: IEEE, 2005. 1130-1134
    [27] Wang F Y. Parallel control and management for intelligent transportation systems: concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3): 630-638
    [28] Kosmatopoulos E B, Papageorgiou M, Wang Y B, Papamichail I, Kouvelas A. AFT2: An automated maintenance and calibration tool for traffic management & control systems. In: Proceedings of the 11th IEEE International Conference on Intelligent Transportation Systems. Beijing, China: IEEE, 2008. 97-104
    [29] Kotsialos A, Papageorgiou M, Diakaki C, Pavlis Y, Middelham F. Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET. IEEE Transactions on Intelligent Transportation Systems, 2002, 3(4): 282-292
    [30] Wong S C, Yang C, Lo H K. A path-based traffic assignment algorithm based on the TRANSYT traffic model. Transportation Research Part B: Methodological, 2001, 35(2): 163-181
    [31] Wong S C, Wong W T, Leung C M, Tong C O. Group-based optimization of a time-dependent TRANSYT traffic model for area traffic control. Transportation Research Part B: Methodological, 2002, 36(4): 291-312
    [32] Bhat C R, Guo J, Srinivasan S, Pinjari A, Eluru N, Copperman R, Sener I. The Comprehensive Econometric Microsimulator for Daily Activity-travel Patterns (CEMDAP), Report 4080-S, Prepared for the Texas Department of Transportation, USA, 2006
    [33] Guo J Y, Bhat C R. Population synthesis for microsimulating travel behavior. Transportation Research Record, 2007, 2014/2007: 92-101
    [34] Chen C M. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 2006, 57(3): 359-377
    [35] Girvan M, Newman M E J. Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(12): 7821-7826
    [36] Dowling R, Skabardonis A, Halkias J, McHale G, Zammit G. Guidelines for calibration of microsimulation models: framework and applications. Transportation Research Record, 2004, 1876: 1-9
    [37] Gomes G, May A, Horowitz R. Congested freeway microsimulation model using VISSIM. Transportation Research Record, 2004, 1876: 71-81
    [38] Munoz L, Gomes G, Yi J G, Toy C, Horowitz R, Alvarez L. Integrated meso-microscale traffic simulation of hierarchical AHS control architectures. In: Proceedings of the 2001 IEEE Intelligent Transportation Systems. Oakland, CA, USA: IEEE, 2001. 82-87
    [39] De Bok M. Estimation and validation of a microscopic model for spatial economic effects of transport infrastructure. Transportation Research Part A: Policy and Practice, 2009, 43(1): 44-59
    [40] De Bok M, Bliemer M C J. Infrastructure and firm dynamics: calibration of microsimulation model for firms in the Netherlands. Transportation Research Record, 2006, 1977: 132-144
    [41] Hoogendoorn S P, Hauser M, Rodrigues N. Applying microscopic pedestrian flow simulation to railway station design evaluation in Lisbon, Portugal. Transportation Research Record, 2004, 1878: 83-94
    [42] Pel A J, Bliemer M C J, Hoogendoorn S P. A review on travel behaviour modelling in dynamic traffic simulation models for evacuations. Transportation, 2012, 39(1): 97-123
    [43] Abdelghany A, Abdelghany K, Mahmassani H S, Al-Gadhi S A. Microsimulation assignment model for multidirectional pedestrian movement in congested facilities. Transportation Research Record, 2005, 1939: 123-132
    [44] Zhang X, Chang G L. Cellular automata-based model for simulating vehicular-pedestrian mixed flows in a congested network. Transportation Research Record, 2011, 2234: 116-124
    [45] Chou C S, Miller-Hooks E. Simulation-based secondary incident filtering method. Journal of Transportation Engineering ---ASCE, 2010, 136(8): 746-754
    [46] Dundovic C, Basch D, Dobrota D. Simulation method for evaluation of LNG receiving terminal capacity. Promet-Traffic and Transportation, 2009, 21(2): 103-112
    [47] Mostarac N, Pavlin S, Mostarac P. Simulation model of civil-military joint use of Zadar airport Manoeuvering area. Promet-Traffic and Transportation, 2008, 20(5): 309-315
    [48] Hu T Y, Liao T Y. An empirical study of simulation-based dynamic traffic assignment procedures. Transportation Planning and Technology, 2011, 34(5): 467-485
    [49] Liao T Y, Hu T Y, Chen L W, Ho W M. Development and empirical study of real-time simulation-based dynamic traffic assignment model. Journal of Transportation Engineering——ASCE, 2010, 136(11): 1008-1020
    [50] Hu T Y, Ho W M. Simulation-based travel time prediction model for traffic corridors. In: Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems. St Louis, MO, USA: IEEE, 2009. 1-6
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基于社会网络视角的交通仿真和计算实验研究文献分析

doi: 10.3724/SP.J.1004.2013.01402
    基金项目:

    国家自然科学基金(60921061, 70890084, 60974095, 60904057, 612 03166)资助

    作者简介:

    叶佩军 中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生. 2008年获大连交通大学电气信息学院自动化专业学士学位. 主要研究方向为平行交通管控,交通流建模与优化,交通仿真,智能交通系统.E-mail: dreamflight@163.com

摘要: 交通仿真和计算实验作为交通科学问题研究和工程应用实践中的重要方法和手段, 受到越来越多的关注.本文从社会网络角度, 分析了2000年~2012年来, ISI Web of Science (WoS) 收录的关于交通仿真及计算实验研究的文献. 本文工作分为三部分: 首先, 分析了近13年来该领域每年论文的发表趋势; 其次, 引入社会网络, 从论文数量、影响力、合作关系和知识传播度四个方面考察了关键学者, 并给出了其合作关系聚类图; 最后, 仍然从上述四个方面考察了本领域内关键的研究机构. 结果表明: 该领域的研究成果和研究机构增长迅速; 学者之间的合作关系非常广泛且极为复杂; 合作的广泛程度与知识传播度并无明显的相关性; 研究机构层面的合作呈现分散状态, 且合作单位数量较少.

English Abstract

叶佩军, 吕宜生, 吉竟初. 基于社会网络视角的交通仿真和计算实验研究文献分析. 自动化学报, 2013, 39(9): 1402-1412. doi: 10.3724/SP.J.1004.2013.01402
引用本文: 叶佩军, 吕宜生, 吉竟初. 基于社会网络视角的交通仿真和计算实验研究文献分析. 自动化学报, 2013, 39(9): 1402-1412. doi: 10.3724/SP.J.1004.2013.01402
YE Pei-Jun, Lü Yi-Sheng, JI Jing-Chu. Literature Analysis for Traffic Simulation and Computational Experiments Based on Social Networks. ACTA AUTOMATICA SINICA, 2013, 39(9): 1402-1412. doi: 10.3724/SP.J.1004.2013.01402
Citation: YE Pei-Jun, Lü Yi-Sheng, JI Jing-Chu. Literature Analysis for Traffic Simulation and Computational Experiments Based on Social Networks. ACTA AUTOMATICA SINICA, 2013, 39(9): 1402-1412. doi: 10.3724/SP.J.1004.2013.01402
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